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- W4288364044 abstract "Probabilistic programs are key to deal with uncertainty in e.g. controller synthesis. They are typically small but intricate. Their development is complex and error prone requiring quantitative reasoning over a myriad of alternative designs. To mitigate this complexity, we adopt counterexample-guided inductive synthesis (CEGIS) to automatically synthesise finite-state probabilistic programs. Our approach leverages efficient model checking, modern SMT solving, and counterexample generation at program level. Experiments on practically relevant case studies show that design spaces with millions of candidate designs can be fully explored using a few thousand verification queries." @default.
- W4288364044 created "2022-07-29" @default.
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- W4288364044 date "2019-04-28" @default.
- W4288364044 modified "2023-09-30" @default.
- W4288364044 title "Counterexample-Driven Synthesis for Probabilistic Program Sketches" @default.
- W4288364044 doi "https://doi.org/10.48550/arxiv.1904.12371" @default.
- W4288364044 hasPublicationYear "2019" @default.
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